DTA was first proposed in [1]}.
It considers set classifiers and predicts the label of a test set by maximizing its expected measure.
Thus it needs the entire joint distribution of the ground-truth which can be estimated by
using the maximum likelihood principle or the logistic regression model.
Several methods have been proposed to solve this type of problems, based on the assumption that the labels
are independent [1]}, [3]}, [4]}.
Later, in [5]}, [6]} this assumption was removed, and a general algorithm was given
which needs only \(n^2+1\)  parameters of the joint distribution, where \(n\)  is the sample size.
The above work focused on the \(F_\beta \)  metric. Recently, a general analysis of DTA was provided
in [7]} for non-decomposable performance measures.
It is also named the Expected Test Utility (ETU) in [8]}.
